Chain-of-Thought enhanced intelligent generation method of electromechanical equipment operation and maintenance schemes

doi:10.3785/j.issn.1008-973X.2026.07.014

Journal of ZheJiang University (Engineering Science)

2026, Vol. 60

Issue (7): 1515-1527 DOI: 10.3785/j.issn.1008-973X.2026.07.014

Chain-of-Thought enhanced intelligent generation method of electromechanical equipment operation and maintenance schemes

Yicong GAO1(

),Dong WU1,Shanghua MI2,*(

),Hao ZHENG2,Jianrong TAN1

1. State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
2. Hangzhou Innovation Institute of Beihang University, Hangzhou 310056, China

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Abstract

An intelligent generation method of electromechanical equipment operation and maintenance schemes with enhanced Chain-of-Thought was proposed, aiming at the problems of low efficiency and poor traceability of operation and maintenance schemes based on manual experience caused by the complex structure of electromechanical equipment and the high coupling degree of faults. Utilizing the capabilities of multi-source knowledge fusion and knowledge reasoning of large language models, the preprocessing process of multi-source heterogeneous operation and maintenance domain knowledge of electromechanical equipment was designed, and the knowledge ontology model of the operation and maintenance domain of electromechanical equipment with enhanced Chain-of-Thought was established. Through the injection of fault knowledge with enhanced Chain-of-Thought and the fine-tuning of large models, the Chain-of-Thought enhanced domain model with causal chain reasoning ability was constructed. The fault traceability reasoning of “fault phenomenon - cause ranking - scheme generation” for electromechanical equipment has been realized. The graph retrieval-augmented generation technology was introduced to construct a components knowledge graph with community division. The multi-component maintenance knowledge was deeply integrated and reasoned, which improved the generation quality of operation and maintenance schemes and achieved an intelligent operation and maintenance closed loop from fault tracking to operation and maintenance scheme generation. Finally, the performance evaluation and application verification of the Chain-of-Thought enhanced domain model were carried out. The results show that the proposed method demonstrates excellent performance in tasks such as fault tracking and operation and maintenance scheme generation, significantly improving the accuracy of fault tracking and the rationality of operation and maintenance schemes.

Key words： Chain-of-Thought enhancement knowledge graph large language model electromechanical equipment operation and maintenance operation and maintenance schemes

Received: 20 May 2025 Published: 23 May 2026

CLC:

TP 391.1

Fund: 国家自然科学基金资助项目(52375272，52575263）；浙江省高等教育学会高等教育研究课题（KT2025456）；杭州市农业与社会发展项目（20241203A21）.

Corresponding Authors: Shanghua MI E-mail: gaoyicong@zju.edu.cn;393185319@qq.com

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Cite this article:

Yicong GAO,Dong WU,Shanghua MI,Hao ZHENG,Jianrong TAN. Chain-of-Thought enhanced intelligent generation method of electromechanical equipment operation and maintenance schemes. Journal of ZheJiang University (Engineering Science), 2026, 60(7): 1515-1527.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2026.07.014 OR https://www.zjujournals.com/eng/Y2026/V60/I7/1515

思维链增强的机电装备运维方案智能生成方法

针对机电装备结构复杂、故障耦合度高导致基于人工经验的运维方案方法效率低、故障追溯性差的问题，提出思维链增强的机电装备运维方案智能生成方法. 利用大语言模型的多源知识融合和知识推理的能力，设计机电装备多源异构运维领域知识的预处理流程，建立思维链增强的机电装备运维领域知识本体模型，通过思维链增强的故障知识注入和大模型微调，构建具备因果链式推理能力的思维链增强领域模型，实现机电装备的“故障现象-原因排序-方案生成”故障溯源推理. 引入图检索增强生成技术，构建具有社区划分的零部件知识图谱，深度融合推理多部件维修知识，提高运维方案的生成质量，实现故障溯源到运维方案生成的智能运维闭环. 对思维链增强领域模型进行性能评估和应用验证，结果表明，所提方法在故障溯源、运维方案生成的任务中表现出优异性能，显著提升了机电装备故障溯源的准确性和运维方案的合理性.

关键词： 思维链增强, 知识图谱, 大语言模型, 机电装备运维, 运维方案

Fig.1 Framework of CoT enhanced intelligent generation method of electromechanical equipment operation and maintenance schemes

Fig.2 Multi-source heterogeneous data preprocessing

Tab.1 De-colloquial data of operation and maintenance text

Tab.2 Intelligent stereoscopic garage electromechanical system entity and relationship

Fig.3 Intelligent stereoscopic garage electromechanical system knowledge ontology model

Fig.4 CoT enhanced fault tracing model for electromechanical equipment

Tab.3 Fault knowledge extraction prompt template

Fig.5 Knowledge storage in Neo4j

Fig.6 CoT enhanced fault tracing process

Fig.7 Fault knowledge graph query and fault matching

Fig.8 Community reporting requirement

Fig.9 CoT enhanced intelligent generation flowchart of electromechanical equipment operation and maintenance schemes

Tab.4 Fine-tuning experiment setup

Tab.5 Comparison between CoT-enhanced model and baseline models

模型	回答
模型	智能车库升降机对中运行超时是什么原因，该怎么排查？
GPT-4	1.导轨或链条卡滞，解决方案：检查并清理导轨、链条、钢丝绳，必要时更换损坏部件. 2.传动机构松动或磨损，解决方案：检查电机、减速机、链轮等，拧紧松动部件或更换磨损部件. 3.升降平台平衡性问题，解决方案：调整平衡块，重新校准负载分布. 4.位置传感器故障或偏移，解决方案：检查并清洁传感器，重新标定或更换损坏的传感器. 5.PLC程序异常，解决方案：检查控制逻辑，调整时间参数，必要时更新或重写程序. 6.变频器或伺服控制异常，解决方案：检查变频器故障代码，调整参数，检测伺服电机编码器反馈. 7.电缆连接松动或损坏，解决方案：检查所有接线，修复松动或断裂的电缆. 8.负载过大或分布不均，解决方案：检查是否超载，重新分配载荷，避免偏载运行.
Llama3-8B	智能车库升降机对中运行超时，可能的原因有多种，包括但不限于以下几点： 1.电源问题：电源不稳定或电压不足可能导致升降机运行缓慢，甚至超时. 排查时可以检查电源线路、电压稳定性和电源设备是否正常工作. 2.控制系统故障：升降机的控制系统负责发送和接收指令，监控运行状态. 如果控制系统出现故障，可能导致指令无法正确执行或状态无法及时反馈，从而引起超时. 此时，需要检查控制系统的硬件和软件是否正常，包括传感器、控制器和执行器等部件. 3.机械部件卡滞：升降机的导轨、链条、滑轮等机械部件如果出现卡滞或磨损，也可能影响运行速度，导致超时. 定期对这些部件进行清洗、润滑和维护，以及检查其磨损程度，是预防此类问题的重要措施. 4.传感器故障：传感器负责检测升降机的位置和状态，如果传感器出现故障或误判，可能导致控制系统做出错误的决策，从而引起超时. 排查时需要检查传感器的灵敏度、准确性和连接线路是否正常.
领域模型（CoT enhanced）	根据运维知识图谱中的常见故障原因及其出现频次，以下是可能原因的排序和排查过程： 1. 最有可能的故障原因：车辆停放较斜，导致对中阻力增大，影响对中完成. 排查过程：检查车辆是否停放歪斜，导致升降机在对中过程中受阻；观察存车位置，确保车辆完全驶入存车位并居中停放；测试不同车辆在相同位置存取，排除个别车辆形状影响. 2. 中等可能原因：对中光电传感器检测异常，导致超时报警排查过程：检查光电传感器是否有灰尘、异物遮挡或对准偏差；检测光电开关信号，确认其是否能正确感应到对中完成状态；观察光电传感器安装角度，调整为最佳检测位置. 3. 最小可能原因：对中整流器损坏，导致信号无法正确传输排查过程：检查对中整流器是否有明显的损坏或老化迹象；使用万用表检测整流器输出电压，确认是否正常；检查整流器连接线路，排除接触不良或断线情况. 按照以上排查顺序，可以优先解决高频故障，逐步排除其他可能性，以快速恢复升降机正常运行.

Tab.6 Comparison of answering performance of different models

Fig.10 System question answering example


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